README: Vertical Symbolic Regression via Deep Policy Gradient#

Directory

Dataset

• data: the generated dataset. Every file represents a ground-truth expression.
• src/scibench: the data oracle API to draw data.

Prerequisite of using these methods

• install the dependency package

pip install -r requirements.txt

• install our data oracle

cd src/scibench
pip install -e .

Methods

• cvDSO: the proposed method.
• ProGED: from https://github.com/brencej/ProGED.
• SPL: symbolic physics learner, from https://github.com/isds-neu/SymbolicPhysicsLearner.
• E2E: End to end transformer for symbolic regression, from https://github.com/facebookresearch/symbolicregression.
• gp_and_cvgp: genetic programming (GP) and VSR-GP algorithm, from https://github.com/jiangnanhugo/cvgp
• dso_classic: the codebase for DSR, VPG, PQT and GPMeld, from https://github.com/dso-org/deep-symbolic-optimization
• odeformer:

Extra

• plots: the jupyter notebook to generate our figure.
• result: contains all the output of all the programs, the training logs.

3. Look at the summarized result

Just open the result and plots folders.